Blind SISR has attracted increasing attention due to its significance in real-world applications, which aims to super-resolved LR images with unknown degradation. According to the ways of degradation modelling, they can be simply divided into two categories: explicit degradation modeling methods and implicit degradation modeling methods. Among them, explicit degradation modeling methods can be further divided into two categories according to whether they use the kernel estimation technology. For instance, Zhang et al. proposed a simple and scalable deep CNN framework for multiple degradation (SRMD [1]}) learning. In SRMD, the concatenated LR image and degradation maps are taken as input after the dimensionality stretching strategy. In DPSR [2]}, deep super-resolver can be used as a prior with a new degradation model, in order to handle LR images with arbitrary blur kernels. After that, UDVD [3]}, AMNet [4]}, USRNet[5]}, and a series of blind SISR methods are proposed by using the degradation map as an additional input for SR images reconstruction. In contrast, some blind SISR methods pay attention to the kernel estimation along with the SR process [6]}, [7]}, [8]}, [9]}. For example, in IKC [6]}, the iterative kernel correction procedure is proposed to help the blind SISR task to find more accurate blur kernels. In DAN [7]}, Luo et al. adopted an alternating optimization algorithm to estimate blur kernel and restore SR image in a single network, which makes the restorer and estimator be well compatible with each other, and thus achieves good results in kernel estimation. However, the reconstruction accuracy of the above methods greatly depends on the accuracy of the degradation mode estimation. To address this issue, more implicit degradation modeling methods are proposed [12]}, [13]}, [14]}, which aim to implicitly learn the potential degradation modes by the external datasets.
